Supervised classification is a fruitful field that has attracted the attention of researchers for many years. One of the methods that has experienced a more in depth transformation in the last years is classification trees. Since the pioneer contribution by Breiman et al. [1]}, where CART was proposed, this technique has included tools from mathematical optimization giving rise to the so called OCT [2]}, [3]}, methods that are optimal in some sense. In spite of that, there is still some more room for further improvements, in particular making classifiers more robust against perturbed datasets. Our contribution is this paper goes in that direction and it augments the catalogue of classification tree methods able to handle noise in the labels of the dataset.
